python - Python OpenCV用于模板匹配

Question

我有一个使用openCV的小python脚本，该脚本在图像中进行模板匹配，并很好地返回一个边界框，如下所示。该脚本的大小也不变，这使其更强大。

给定返回的边界框，如何用其他模板将替换为并保存更改后的图像？

这是original template和main image



现在，我想简单地将精确的框替换为下面调整后的版本的template2，然后保存新图像。我怎样才能做到这一点？



这是我的简单代码:

# USAGE
# python match.py --template cod_logo.png --images images

# import the necessary packages
import numpy as np
import argparse
import imutils
import glob
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t", "--template", required=True, help="Path to template image")
ap.add_argument("-i", "--images", required=True,
    help="Path to images dir where template will be matched")
ap.add_argument("-v", "--visualize",
    help="Flag 0 or 1 indicating whether or not to visualize each iteration")
args = vars(ap.parse_args())

# load the image image, convert it to grayscale, and detect edges
template = cv2.imread(args["template"])
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape[:2]
cv2.imshow("Template", template)

# loop over the images to find the template in
for imagePath in glob.glob(args["images"] + "/*.*"):
    # load the image, convert it to grayscale, and initialize the
    # bookkeeping variable to keep track of the matched region
    image = cv2.imread(imagePath)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    found = None

    # loop over the scales of the image
    for scale in np.linspace(0.2, 1.0, 20)[::-1]:
        # resize the image according to the scale, and keep track
        # of the ratio of the resizing
        resized = imutils.resize(gray, width = int(gray.shape[1] * scale))
        r = gray.shape[1] / float(resized.shape[1])

        # if the resized image is smaller than the template, then break
        # from the loop
        if resized.shape[0] < tH or resized.shape[1] < tW:
            break

        # detect edges in the resized, grayscale image and apply template
        # matching to find the template in the image
        edged = cv2.Canny(resized, 50, 200)
        result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
        (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

        # check to see if the iteration should be visualized
        if args.get("visualize", False):
            # draw a bounding box around the detected region
            clone = np.dstack([edged, edged, edged])
            cv2.rectangle(clone, (maxLoc[0], maxLoc[1]),
                (maxLoc[0] + tW, maxLoc[1] + tH), (0, 0, 255), 2)
            cv2.imshow("Visualize", clone)
            cv2.waitKey(0)

        # if we have found a new maximum correlation value, then ipdate
        # the bookkeeping variable
        if found is None or maxVal > found[0]:
            found = (maxVal, maxLoc, r)

    # unpack the bookkeeping varaible and compute the (x, y) coordinates
    # of the bounding box based on the resized ratio
    (_, maxLoc, r) = found
    (startX, startY) = (int(maxLoc[0] * r), int(maxLoc[1] * r))
    (endX, endY) = (int((maxLoc[0] + tW) * r), int((maxLoc[1] + tH) * r))

    # draw a bounding box around the detected result and display the image
    cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2)
    cv2.imshow("Image", image)
    cv2.waitKey(0)

Answer 1

我做了一些改变...
1]我没有使用参数解析器
2] tepmlate2是反恐精英。
3] Image2是反击在COD顶部的图像。

STEPS : Extracting roi (region of interest) , then resizing the new image accordingly ... , then operlapping roi with new resized image, placing roi back on the image2.

Advantage => you can change the opacity of the roi and template by changing alpha and beta in addWeighted.

# USAGE
# python match.py --template cod_logo.png --images images

# import the necessary packages
import numpy as np
import argparse
import imutils
import glob
import cv2

#New template
template2 = cv2.imread("template2.png")

# construct the argument parser and parse the arguments
# ap = argparse.ArgumentParser()
# ap.add_argument("-t", "--template", required=True, help="Path to template image")
# ap.add_argument("-i", "--images", required=True,
#     help="Path to images dir where template will be matched")
# ap.add_argument("-v", "--visualize",
#     help="Flag 0 or 1 indicating whether or not to visualize each iteration")
# args = vars(ap.parse_args())

# load the image image, convert it to grayscale, and detect edges
template = cv2.imread("template.png")
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template, 50, 200)
(tH, tW) = template.shape\[:2\]
cv2.imshow("Template", template)

# loop over the images to find the template in
#for imagePath in glob.glob(args\["images"\] + "/*.*"):
    # load the image, convert it to grayscale, and initialize the
    # bookkeeping variable to keep track of the matched region
image = cv2.imread("mainImage.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
found = None

# loop over the scales of the image
for scale in np.linspace(0.2, 1.0, 20)\[::-1\]:
    # resize the image according to the scale, and keep track
    # of the ratio of the resizing
    resized = imutils.resize(gray, width = int(gray.shape\[1\] * scale))
    r = gray.shape\[1\] / float(resized.shape\[1\])

    # if the resized image is smaller than the template, then break
    # from the loop
    if resized.shape\[0\] < tH or resized.shape\[1\] < tW:
        break

    # detect edges in the resized, grayscale image and apply template
    # matching to find the template in the image
    edged = cv2.Canny(resized, 50, 200)
    result = cv2.matchTemplate(edged, template, cv2.TM_CCOEFF)
    (_, maxVal, _, maxLoc) = cv2.minMaxLoc(result)

    # check to see if the iteration should be visualized
    '''
    if args.get("visualize", False):
        # draw a bounding box around the detected region
        clone = np.dstack(\[edged, edged, edged\])
        cv2.rectangle(clone, (maxLoc\[0\], maxLoc\[1\]),
            (maxLoc\[0\] + tW, maxLoc\[1\] + tH), (0, 0, 255), 2)
        cv2.imshow("Visualize", clone)
        cv2.waitKey(0)'''

    # if we have found a new maximum correlation value, then ipdate
    # the bookkeeping variable
    if found is None or maxVal > found\[0\]:
        found = (maxVal, maxLoc, r)

# unpack the bookkeeping varaible and compute the (x, y) coordinates
# of the bounding box based on the resized ratio
(_, maxLoc, r) = found
(startX, startY) = (int(maxLoc\[0\] * r), int(maxLoc\[1\] * r))
(endX, endY) = (int((maxLoc\[0\] + tW) * r), int((maxLoc\[1\] + tH) * r))

#MY CODE
image2 = image.copy()
resizedTemplate = cv2.resize(template2, (endX-startX, endY-startY), interpolation = cv2.INTER_AREA)
roi = image2[startY:endY, startX:endX]
img = cv2.addWeighted(resizedTemplate, 1, roi, 0, 0)
image2[startY:endY, startX:endX] = img


# draw a bounding box around the detected result and display the image
cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 128), 2)
cv2.imshow("Image", image)
cv2.imshow("Image2", image2)
#cv2.imshow("resizedTemplate", resizedTemplate)
cv2.waitKey(0)

results